Abstract Rift Valley fever virus causes disease in humans and livestock throughout Africa and into the Middle East. It is an arbovirus of both clinical and agricultural significance that is designated as a high priority pathogen for research and development by WHO and NIAID. Currently, our understanding of the pathogenesis of this virus is limited by the lack of a mouse model in which human disease is accurately recapitulated. Human disease is characterized by several distinct clinical syndromes, a self-limiting non-specific febrile illness, hepatitis, hemorrhagic fever, and neurologic disease. In contrast, infection of inbred mice results in rapid disseminated disease that is uniformly lethal. Data from human studies has demonstrated an association between single nucleotide polymorphisms in innate immune signaling genes and variable clinical manifestations, suggesting that host genetic differences could be contributing to disease outcome. Even within the typical clinical manifestations seen in inbred mouse models, some variability exists in that the C57BL/6 mouse succumbs in 3-4 days while the BALB/c succumbs in 9-10 days, and this is associated with differences in innate immune pathways. These data suggest that it is possible to identify mouse models that exhibit a spectrum of RVFV disease phenotypes. Therefore, the genetically outbred Collaborative Cross resource provides an ideal system in which to identify more varied mouse models for RVFV pathogenesis studies. In the proposed studies we will evaluate various strains of mice for clinical, immunological and virologic differences. We will use a panel of biomarkers that we previously defined in humans infected with RVFV and evaluate their utility in predicting disease manifestations in the mouse model. Using mouse strains of CC mice that exhibit variable susceptibility or disease manifestations following RVFV infection, we will validate the biomarker panel for predicting disease. We anticipate that these studies will identify a mouse model that better recapitulates human disease as well as a biomarker panel that reflects the pathophysiology at work in the disease process. Finally, if this biomarker panel proves useful in predicting disease outcome it could be further developed as a trigger for therapeutic intervention or for use as a way to monitor response to therapy in humans.